PD6646

PD6646 Sample Summary

## `summarise()` has grouped output by 'patient', 'age_at_sample_exact', 'age_at_sample', 'DOB', 'DATE_OF_DIAGNOSIS'. You can override using the `.groups` argument.

## Joining, by = "PDID"

	patient	ID	age_at_sample_exact	cell_type	phase	BaitLabel
3	PD6646	PD6646n	76.44353	PB Gran	Recapture	PD6646n
4	PD6646	PD6646o	78.97331	PB Gran	Recapture	PD6646o
5	PD6646	PD6646p	80.14237	PB Gran	Recapture	PD6646p
1	PD6646	COLONY81	81.01027	BFU-E-Colony	Colony	NA
6	PD6646	PD6646q	82.97878	PB Gran	Recapture	PD6646q
2	PD6646	COLONY85	84.70089	BFU-E-Colony	Colony	NA

Tree

tree=plot_basic_tree(PD$pdx,label = PD$patient,style="classic")

Expanded Tree with Node Labels

The nodes in this plot can be cross-referenced with nodes specified in subsequent results. The plot also serves to give an idea of what the topology at the top of the tree looks like.

tree=plot_basic_tree(expand_short_branches(PD$pdx,prop = 0.1),label = PD$patient,style="classic")
node_labels(tree)

Timing of driver mutations (using Model = poisson_tree )

Note that the different colours on the tree indicate the separately fitted mutation rate clades.

Driver Specific Mutation Rates & Telomere Lengths by Colony & Timepoint

## 
## Random-Effects Model (k = 1; tau^2 estimator: REML)
## 
##   logLik  deviance       AIC       BIC      AICc 
##   0.0000   -0.0000    4.0000      -Inf   16.0000   
## 
## tau^2 (estimated amount of total heterogeneity): 0
## tau (square root of estimated tau^2 value):      0
## I^2 (total heterogeneity / total variability):   0.00%
## H^2 (total variability / sampling variability):  1.00
## 
## Test for Heterogeneity:
## Q(df = 0) = 0.0000, p-val = 1.0000
## 
## Model Results:
## 
## estimate      se     zval    pval    ci.lb    ci.ub 
##  19.3909  0.6033  32.1417  <.0001  18.2085  20.5734  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'patient'. You can override using the `.groups` argument.

node	driver	status	child_count	type	colony_count	mean_lambda_rescaled	correction	sd_rescaled	lb_rescaled	ub_rescaled	median_rescaled	p_lt_wt
-1	WT	1	-1	local	18	19.39092	1.014996	0.3639650	18.69665	20.12276	19.38593	NA
121	DNMT3A	1	99	local	8	18.68510	1.014996	0.4727588	17.79205	19.65477	18.67391	0.870800
142	JAK2:DNMT3A	1	77	local	73	22.60973	1.014996	1.5523421	19.71770	25.75073	22.56640	0.016100
175	9pUPD:JAK2:DNMT3A	0	3	local	3	25.39854	1.014996	1.9432401	21.78597	29.39981	25.33098	0.000375
27	9pUPD:JAK2:DNMT3A	0	1	local	1	21.37484	1.014996	3.8666001	12.74849	28.33233	21.68363	0.266800
124	CBL:DNMT3A	1	14	local	14	18.26586	1.014996	0.7048151	16.92716	19.71524	18.24567	0.914675

Driver Acquisition Timeline

All ages are in terms of post conception years. The vertical red lines denote when colonies were sampled and blue lines when targeted follow up samples were taken.

patient	node	driver	child_count	lower_median	upper_median	lower_lb95	lower_ub95	upper_lb95	upper_ub95	N	group	age_at_diagnosis_pcy	max_age_at_sample	min_age_at_sample
PD6646	121	DNMT3A	99	0.0110787	27.17183	0.0082774	0.0254408	24.99919	29.40281	6	DNMT3A	76.80767	85.42916	77.1718
PD6646	124	CBL	14	32.1642463	50.72960	29.9563203	34.4032654	48.41968	52.95501	6	CBL	76.80767	85.42916	77.1718
PD6646	142	JAK2	77	29.6007868	63.17088	27.4336204	31.8099078	60.35690	65.61309	6	JAK2	76.80767	85.42916	77.1718
PD6646	175	9pUPD	3	67.3874939	72.79130	65.1055774	69.3451450	71.02458	74.30215	6	9pUPD	76.80767	85.42916	77.1718

Copy Number Variation and Timing

Summary of LOH timing inference

## Timings using the Clade Specific Rates

label	node	het.sensitivity	chr	start	end	nhet	nhom	mean_loh_event	lower_loh_event	upper_loh_event	t_before_end	t_before_end_lower	t_before_end_upper	kb	count_in_bin	count_se	pmut	pmut_se	xmean	xse_mean	xsd	x2.5.	x50.	x97.5.	xn_eff	xRhat	lmean	lse_mean	patient	driver3	child_count
9pUPD_A	175	0.9629	9	10469	5464376	0	0	70.26	67.53	72.64	2.497	0.1131	5.23	5400000	838	28.95	0.00183	6.322e-05	0.5387	0.002153	0.2867	0.0336	0.5591	0.9791	17730	1	1.000	4.126e-07	PD6646	9pUPD:JAK2:DNMT3A	3
9pUPD_B	27	0.7240	9	10469	33800406	1	2	NA	NA	NA	NA	NA	NA	33800000	6732	82.05	0.01470	1.792e-04	0.6540	0.001191	0.1897	0.2448	0.6774	0.9478	25378	1	6.447	4.901e-04	NA	NA	NA

Duplications?

VAF Distribution of Targeted Follow Up Samples

Here we exclude all local CNAs and depict as color VAF plots